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  1. PO
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    <p>You're using different bins in the two cases. In your case, <code>np.linspace(xmin, xmax, (xmax-xmin)/step)</code> has 5 bins, but you've told <code>plt.hist</code> to use 6 bins.</p> <p>You can see this by looking at the output of each:</p> <pre><code>h1 = np.histogram(s, bins=np.linspace(xmin, xmax, (xmax-xmin)/step)) h_plt = plt.hist(s, bins=6,alpha=0.5) </code></pre> <p>Then:</p> <pre><code>&gt;&gt;&gt; h1[1] array([ 1. , 2.2, 3.4, 4.6, 5.8, 7. ]) &gt;&gt;&gt; h_plt[1] array([ 1., 2., 3., 4., 5., 6., 7.]) </code></pre> <p>I would use:</p> <pre><code>y, x = np.histogram(s, bins=np.linspace(xmin, xmax, (xmax-xmin)/step)) nbins = y.size # ... plt.hist(s, bins=nbins, alpha=0.5) </code></pre> <p>Then your histograms match, but your plot still won't because you've plotted the output of your <code>np.histogram</code> at the centers of the bins, but <code>plt.bar</code> expects an array of the left edges:</p> <blockquote> <p><code>plt.bar(left, height, width=0.8, bottom=None, hold=None, **kwargs)</code> </p> <p>Parameters<br> <code>----------</code><br> <code>left</code> : sequence of scalars<br> the <code>x</code> coordinates of the left sides of the bars</p> <p><code>height</code> : sequence of scalars<br> the heights of the bars</p> </blockquote> <p>What you want is:</p> <pre><code>import numpy as np import matplotlib.pyplot as plt s = [1,1,1,1,2,2,2,3,3,4,5,5,5,6,7,7,7,7,7,7,7] xmin = 1 xmax = 7 step = 1 y, x = np.histogram(s, bins=np.linspace(xmin, xmax, (xmax-xmin)/step)) nbins = y.size plt.bar(x[:-1], y, width=x[1]-x[0], color='red', alpha=0.5) plt.hist(s, bins=nbins, alpha=0.5) plt.grid(True) plt.show() </code></pre> <p><img src="https://i.stack.imgur.com/fIMtW.png" alt="two hists"></p>
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    1. POWhat is the difference between `np.histogram` and `plt.hist`? Why don't these commands plot the same graphics?
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    1. USaskewchan
    1. USaskewchan
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    1. POWhat is the difference between `np.histogram` and `plt.hist`? Why don't these commands plot the same graphics?
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    1. VO
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    1. CONow I see that I had wrong way with centering bins by their edges. You just skip the last value and it is more clear and simple, thanks a lot!
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      1. USaestet
    2. COYour'e welcome! I didn't mention in the answer, but to find the centers of the bins, if `x` is a numpy array (which it is by default if you don't call `x.tolist()`, then you can say: `centers = (x[1:] - x[:-1])/2.`
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    3. COthank you again, done.
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